From dfec9471ea3153cb63c88d0a8d6e326ce6989e34 Mon Sep 17 00:00:00 2001
From: Ross Girshick <rbg@eecs.berkeley.edu>
Date: Tue, 4 Feb 2014 11:00:55 -0800
Subject: [PATCH] cleanup matlab demo

---
 matlab/caffe/matcaffe_demo.m         | 20 ++++++++++------
 matlab/caffe/matcaffe_demo_weights.m | 35 ----------------------------
 2 files changed, 13 insertions(+), 42 deletions(-)
 delete mode 100644 matlab/caffe/matcaffe_demo_weights.m

diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/caffe/matcaffe_demo.m
index 8b13e07a..ff27f970 100644
--- a/matlab/caffe/matcaffe_demo.m
+++ b/matlab/caffe/matcaffe_demo.m
@@ -1,4 +1,4 @@
-function scores = matcaffe_demo(im, use_gpu)
+function [scores, layers] = matcaffe_demo(im, use_gpu)
 % scores = matcaffe_demo(im, use_gpu)
 %
 % Demo of the matlab wrapper using the ILSVRC network.
@@ -11,7 +11,7 @@ function scores = matcaffe_demo(im, use_gpu)
 %   scores   1000-dimensional ILSVRC score vector
 %
 % You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
 %  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
 % Or the equivalent based on where things are installed on your system
 %
@@ -20,12 +20,16 @@ function scores = matcaffe_demo(im, use_gpu)
 %  scores = matcaffe_demo(im, 1);
 %  [score, class] = max(scores);
 
-model_def_file = '../../examples/imagenet/imagenet_deploy.prototxt';
-% NOTE: you'll have to get the pre-trained ILSVRC network
-model_file = '../../examples/imagenet/caffe_reference_imagenet_model';
-
 % init caffe network (spews logging info)
-caffe('init', model_def_file, model_file);
+if caffe('is_initialized') == 0
+  model_def_file = '../../examples/imagenet/imagenet_deploy.prototxt';
+  model_file = '../../examples/imagenet/caffe_reference_imagenet_model';
+  if exist(model_file, 'file') == 0
+    % NOTE: you'll have to get the pre-trained ILSVRC network
+    error('You need a network model file');
+  end
+  caffe('init', model_def_file, model_file);
+end
 
 % set to use GPU or CPU
 if exist('use_gpu', 'var') && use_gpu
@@ -51,6 +55,8 @@ toc;
 scores = reshape(scores{1}, [1000 10]);
 scores = mean(scores, 2);
 
+% you can also get network weights by calling
+layers = caffe('get_weights');
 
 % ------------------------------------------------------------------------
 function images = prepare_image(im)
diff --git a/matlab/caffe/matcaffe_demo_weights.m b/matlab/caffe/matcaffe_demo_weights.m
deleted file mode 100644
index aae15593..00000000
--- a/matlab/caffe/matcaffe_demo_weights.m
+++ /dev/null
@@ -1,35 +0,0 @@
-function layers = matcaffe_demo_weights(use_gpu)
-% layers = matcaffe_demo_weights(use_gpu)
-% 
-% Demo of how to extract network parameters ("weights") using the matlab
-% wrapper.
-%
-% input
-%   use_gpu  1 to use the GPU, 0 to use the CPU
-%
-% output
-%   layers   struct array of layers and their weights
-%
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-if caffe('is_initialized') == 0
-  model_def_file = '../../examples/imagenet_deploy.prototxt';
-  model_file = '../../examples/alexnet_train_iter_470000';
-  caffe('init', model_def_file, model_file);
-end
-
-% set to use GPU or CPU
-if exist('use_gpu', 'var') && use_gpu
-  caffe('set_mode_gpu');
-else
-  caffe('set_mode_cpu');
-end
-
-% put into test mode
-caffe('set_phase_test');
-
-layers = caffe('get_weights');